Compressive 3D ultrasound imaging using a single sensor

Kruizinga, Pieter; Meulen, Pim van der; Fedjajevs, Andrejs; Mastik, Frits; Springeling, Geert; Jong, Nico de; Bosch, Johannes G.; Leus, Geert

doi:10.1126/sciadv.1701423

Cited by 114 publications

(97 citation statements)

References 44 publications

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“…Future investigations may consider reducing the number of active elements down to 64 (or even less! 60 ) to implement ultra-light scanners and this issue could be addressed considering the element size as a new degree of freedom in the optimization to compensate for the extreme sensitivity loss 61 .…”

Section: Discussionmentioning

confidence: 99%

Experimental 3-D Ultrasound Imaging with 2-D Sparse Arrays using Focused and Diverging Waves

Roux¹,

Varray²,

Petrusca³

et al. 2018

Sci Rep

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Three dimensional ultrasound (3-D US) imaging methods based on 2-D array probes are increasingly investigated. However, the experimental test of new 3-D US approaches is contrasted by the need of controlling very large numbers of probe elements. Although this problem may be overcome by the use of 2-D sparse arrays, just a few experimental results have so far corroborated the validity of this approach. In this paper, we experimentally compare the performance of a fully wired 1024-element (32 × 32) array, assumed as reference, to that of a 256-element random and of an “optimized” 2-D sparse array, in both focused and compounded diverging wave (DW) transmission modes. The experimental results in 3-D focused mode show that the resolution and contrast produced by the optimized sparse array are close to those of the full array while using 25% of elements. Furthermore, the experimental results in 3-D DW mode and 3-D focused mode are also compared for the first time and they show that both the contrast and the resolution performance are higher when using the 3-D DW at volume rates up to 90/second which represent a 36x speed up factor compared to the focused mode.

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Section: Discussionmentioning

confidence: 99%

Experimental 3-D Ultrasound Imaging with 2-D Sparse Arrays using Focused and Diverging Waves

Roux¹,

Varray²,

Petrusca³

et al. 2018

Sci Rep

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show abstract

“…5d) will enable more advanced imaging methods. By predicting the pulse-echo signals and random grouping in a linear system matrix, we can formulate the reconstruction as an inverse imaging problem, as we have recently shown in [28] by imaging a 3D volume using only one transducer.…”

Section: Reconfigurabilitymentioning

confidence: 99%

A Reconfigurable Ultrasound Transceiver ASIC With <inline-formula> <tex-math notation="LaTeX">$24\times40$ </tex-math> </inline-formula> Elements for 3-D Carotid Artery Imaging

Kang

Ding

Shabanimotlagh

et al. 2018

IEEE J. Solid-State Circuits

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This paper presents an ultrasound transceiver ASIC designed for 3-D ultrasonic imaging of the carotid artery. This application calls for an array of thousands of ultrasonic transducer elements, far exceeding the number of channels of conventional imaging systems. The 3.6 × 6.8 mm 2 ASIC interfaces a piezo-electric transducer array of 24 × 40 elements, directly integrated on top of the ASIC, to an imaging system using only 24 transmit and receive channels. Multiple ASICs can be tiled together to form an even bigger array. The ASIC, implemented in a 0.18 μm high-voltage BCDMOS process, consists of a reconfigurable switch matrix and row-level receive circuits. Each element is associated with a compact bootstrapped high-voltage transmit switch, an isolation switch for the receive circuits and programmable logic that enables a variety of imaging modes. Electrical and acoustic experiments successfully demonstrate the functionality of the ASIC. In addition, the ASIC has been successfully used in a 3-D imaging experiment.

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“…T is the vector of scatterer amplitudes. This model has been used in B-mode (2-dimensional) [4][5][6][7][8][9], A-mode (1-dimensional) [16,17], and 3-dimensional [18] ultrasound imaging.…”

Section: Model-based Imaging and Regularizationmentioning

confidence: 99%

Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit

Passarin¹,

Pipa²,

Zibetti³

2018

Preprint

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Model-based image reconstruction has brought improvements in terms of contrast and spatial resolution to imaging applications such as magnetic resonance imaging and emission computed tomography. However, their use for pulse-echo techniques like ultrasound imaging is limited by the fact that model-based algorithms assume a finite grid of possible locations of scatterers in a medium -- which does not reflect the continuous nature of real world objects and creates a problem known as off-grid deviation. To cope with this problem, we present a method of dictionary expansion and constrained reconstruction that approximates the continuous manifold of all possible scatterer locations within a region of interest. The expanded dictionary is created using a highly coherent sampling of the region of interest, followed by a rank reduction procedure based on a truncated singular value decomposition. We develop a greedy algorithm, based on the Orthogonal Matching Pursuit (OMP), that uses a correlation-based non-convex constraint set that allows for the division of the region of interest into cells of any size. To evaluate the performance of the method, we present results of 2-dimensional ultrasound image reconstructions with simulated data in a nondestructive testing application. Our method succeeds in the reconstructions of sparse images from noisy measurements, providing higher accuracy than previous approaches based on regular discrete models.

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Compressive 3D ultrasound imaging using a single sensor

Abstract: Compressive 3D ultrasound imaging is possible with only one sensor and a simple aperture coding mask.

Cited by 114 publications

References 44 publications

Experimental 3-D Ultrasound Imaging with 2-D Sparse Arrays using Focused and Diverging Waves

Experimental 3-D Ultrasound Imaging with 2-D Sparse Arrays using Focused and Diverging Waves

A Reconfigurable Ultrasound Transceiver ASIC With <inline-formula> <tex-math notation="LaTeX">$24\times40$ </tex-math> </inline-formula> Elements for 3-D Carotid Artery Imaging

Sparse Ultrasound Imaging via Manifold Low-Rank Approximation and Non-Convex Greedy Pursuit

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